This document proposes a new approach called HERec for recommendations in heterogeneous information networks (HINs). HERec uses meta-path based random walks to generate node sequences for network embedding. The learned embeddings are transformed and integrated into an extended matrix factorization model. Experiments on three datasets show HERec effectively improves rating predictions and performs well for cold-start recommendations by exploiting embedded HIN information.

1. Heterogeneous Information Network Embedding for
Recommendation
ABSTRACT:
Due to the flexibility in modelling data heterogeneity, heterogeneous information
network (HIN) has been adopted to characterize complex and heterogeneous
auxiliary data in recommender systems, called HIN based recommendation. It is
challenging to develop effective methods for HIN based recommendation in both
extraction and exploitation of the information from HINs. Most of HIN based
recommendation methods rely on path based similarity, which cannot fully mine
latent structure features of users and items. In this paper, we propose a novel
heterogeneous network embedding based approach for HIN based
recommendation, called HERec. To embed HINs, we design a meta-path based
random walk strategy to generate meaningful node sequences for network
embedding. The learned node embeddings are first transformed by a set of fusion
functions, and subsequently integrated into an extended matrix factorization (MF)
model. The extended MF model together with fusion functions are jointly
optimized for the rating prediction task. Extensive experiments on three real-world
datasets demonstrate the effectiveness of the HERec model. Moreover, we show
the capability of the HERec model for the cold-start problem, and reveal that the
transformed embedding information from HINs can improve the recommendation
performance.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

2.  System : Pentium Dual Core.
 Hard Disk : 120 GB.
 Monitor : 15’’ LED
 Input Devices : Keyboard, Mouse
 Ram : 1 GB
SOFTWARE REQUIREMENTS:
 Operating system : Windows 7.
 Coding Language : JAVA.
 Tool : Netbeans 7.2.1
 Database : MYSQL
REFERENCE:
Chuan Shi, Member, IEEE, Binbin Hu, Wayne Xin Zhao Member, IEEE and
Philip S. Yu, Fellow, IEEE, “Heterogeneous Information Network Embedding for
Recommendation”, IEEE Transactions on Knowledge and Data Engineering,
Volume: 31 , Issue: 2 , Feb. 1 2019.